
algo:mds summarizes how \(\mathbf {P}^e\)  are computed by optimizing eqn:surrogateobjective. In Line 2, we compute the dissimilarity matrix \(\mathbf {\Delta }\)  by using the maximum distance between the prototype vectors \(\mathbf {P}\) . First \(\mathbf {P}^e\)  is randomly initialised. Until there is negligible change \(\epsilon \)  in \(\mathbf {P}^e\) , we update \(\mathbf {P}^e\)  to optimize the surrogate function \(\tau (\mathbf {P}^e, \mathbf {Y})\) . The resulting vectors in \(\mathbf {P}^e\)  are guaranteed to be equidistant from each other [1]}.
